Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards

Aydin, Nazli Yonca; Düzgün, Şebnem; Wenzel, Friedemann; Heinimann, Hans Rudolf

doi:10.1007/s11069-017-3112-z

Cited by 87 publications

(34 citation statements)

References 50 publications

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“…For example, Aydin et al [2] used the GCC along with betweenness centrality and network efficiency to evaluate the topological resilience of urban road networks under seismic hazards in the Kathmandu metropolitan region, Nepal. Even though it can serve as a common measure to represent the threshold of network percolation subject to random and/or malicious attacks, in this study, we used the GCC as a metric to estimate the connectivity performance of a network.…”

Section: Determining Measures Of Performancementioning

confidence: 99%

“…Another probabilistic resilience evaluation focuses on multiple infrastructure systems and their dependencies [5]. Aydin et al [2] have previously proposed a resilience evaluation method for transportation network topology that integrates graph-based metrics into stress-testing methodologies. Ayyub [3] has provided a detailed overview of the definitions of resilience used within the context of ecology, social sciences, natural hazards etc., as well as the metrics that were available.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Framework for improving the resilience and recovery of transportation networks under geohazard risks

Aydin¹,

Düzgün

Heinimann³

et al. 2018

International Journal of Disaster Risk Reduction

Self Cite

104

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A B S T R A C TRural transportation networks are highly susceptible to geohazards such as earthquakes and landslides. Indirect losses can be severe because the breakdown of a transportation network aggravates rescue, supply, and other recovery activities. The operations and logistics of rural networks that are under seismic risks must be managed using the limited resources specifically in developing countries. We propose a methodology to evaluate road recovery strategies for restoring connectivity after blockages due to earthquakes and earthquake-triggered landslides. This paper gives insight into the recovery process, which can be used by decision-makers for enhancing resilience and supplying immediate relief to rural areas. The proposed framework has four steps: 1) identification of strategies for increasing recovery performance, 2) determination of graph-based metrics to represent network connectivity, 3) applying topology-based and Monte Carlo simulations to each strategy, and 4) analysis of recovery times to compare these resilience-enhancement strategies. The methodology was tested using a case study from Sindhupalchok District, Nepal, a region that was severely affected by the Gorkha earthquake in 2015. The closed road segments and recovery times were determined through field surveys with locals and governmental authorities, and by investigating the intensity of earthquake-triggered landslides. Our results showed that the proposed approach provides information about the recovery behavior of road networks and simplifies the evaluation process. It is robust enough to extend and assess decision-makers' preferences for improving resilience.

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Section: Determining Measures Of Performancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Framework for improving the resilience and recovery of transportation networks under geohazard risks

Aydin¹,

Düzgün

Heinimann³

et al. 2018

International Journal of Disaster Risk Reduction

Self Cite

104

View full text Add to dashboard Cite

show abstract

“…Centrality measures We have chose node betweenness and edge betweenness centrality, which have been advocated to determine the disaster resilience of transportation networks (Aydin et al 2018). The node betweenness centrality indicates the number of shortest paths between any pair of nodes crosses a certain node and the edge betweenness centrality indicates the number of shortest paths between any pair of nodes crosses a certain edge, see Sect.…”

Section: Choice Of Metricsmentioning

confidence: 99%

Humanitarian access, interrupted: dynamic near real-time network analytics and mapping for reaching communities in disaster-affected countries

2020

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In the immediate aftermath of a disaster, local and international aid organisations deploy to deliver life-saving aid to the affected population. Yet pre-disaster road maps and road transportation models do not capture disruptions to the transportation network caused by the disaster or the dynamic changes of the situation, resulting in uncertainty and inefficiency in planning and decision-making. The integration of data in near real time on the status of the road infrastructure in the affected region can help aid organisations to keep track of the rapidly shifting conditions on the ground and to assess the implications for their logistics planning and operations. In this paper, we present a rapid graph-theoretical reachability information system based on a combination of OpenStreetMap and open humanitarian data. The system supports logistics planning in determining road access to affected communities. We demonstrate the results of our approach in a case study on the 2018 earthquake in Papua New Guinea. Our findings show the reachability of affected communities depending on the actual status of the road network, allowing for the prioritization of targeted locations and the identification of alternative routes to get there.

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“…Meanwhile, the majority of research concerning resilience of transport networks focuses on quantitative analysis; to be more specific, these quantitative studies are conducted from two perspectives: topology-based model and mathematical programming-based model [14,15]. In general, topology-based models mainly utilize some indices based on complex network theory to assess resilience, such as average path length [11], betweenness centrality [16], and giant connected component [17]. However, these topology-based measures tend to ignore the realistic characteristics of transport networks such as travel demand, road capacity, and traveller's behaviours, although such measures can be easily understood and efficiently computed.…”

Section: Introductionmentioning

confidence: 99%

Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning

Shang

Chen

et al. 2020

Complexity

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Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve the network performance by adjusting red/green time split. In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one. The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions. This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others.

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Integration of stress testing with graph theory to assess the resilience of urban road networks under seismic hazards

Cited by 87 publications

References 50 publications

Framework for improving the resilience and recovery of transportation networks under geohazard risks

Framework for improving the resilience and recovery of transportation networks under geohazard risks

Humanitarian access, interrupted: dynamic near real-time network analytics and mapping for reaching communities in disaster-affected countries

Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning

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